Meteorologically-Informed Adaptive Conformal Prediction for Tropical Cyclone Intensity Forecasting
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915331955490816 |
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| author | Chen, Xuepeng Luo, Jing-Jia Li, Qingqing Meng, Fan |
| author_facet | Chen, Xuepeng Luo, Jing-Jia Li, Qingqing Meng, Fan |
| contents | Rapid intensification (RI) of tropical cyclones (TCs) poses a great challenge due to their highly nonlinear dynamics and inherent uncertainties. Conventional statistical dynamics and artificial intelligence prediction models typically rely on static parameterization schemes, which limits their ability to capture the non-stationary error structure in the intensity evolution. To address this issue, we propose a physically-inspired covariate adaptive conformal prediction framework that dynamically adjusts uncertainty quantification by incorporating process information such as intensity and evolutionary stage. Our approach not only surpasses state-of-the-art models in point prediction accuracy, but also delivers physically consistent and interpretable forecast intervals, establishing a more process-aware framework for probabilistic prediction of extreme weather events. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06638 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Meteorologically-Informed Adaptive Conformal Prediction for Tropical Cyclone Intensity Forecasting Chen, Xuepeng Luo, Jing-Jia Li, Qingqing Meng, Fan Atmospheric and Oceanic Physics Rapid intensification (RI) of tropical cyclones (TCs) poses a great challenge due to their highly nonlinear dynamics and inherent uncertainties. Conventional statistical dynamics and artificial intelligence prediction models typically rely on static parameterization schemes, which limits their ability to capture the non-stationary error structure in the intensity evolution. To address this issue, we propose a physically-inspired covariate adaptive conformal prediction framework that dynamically adjusts uncertainty quantification by incorporating process information such as intensity and evolutionary stage. Our approach not only surpasses state-of-the-art models in point prediction accuracy, but also delivers physically consistent and interpretable forecast intervals, establishing a more process-aware framework for probabilistic prediction of extreme weather events. |
| title | Meteorologically-Informed Adaptive Conformal Prediction for Tropical Cyclone Intensity Forecasting |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2506.06638 |